Federated Imitation Learning: A Cross-Domain Knowledge Sharing Framework for Traffic Scheduling in 6G Ubiquitous IoT

IEEE Network ◽  
2021 ◽  
Vol 35 (5) ◽  
pp. 136-142
Author(s):  
Ao Yu ◽  
Qingkai Yang ◽  
Lihua Dou ◽  
Mohamed Cheriet
Keyword(s):  
Knowledge Sharing ◽  
Domain Knowledge ◽  
Imitation Learning ◽  
Traffic Scheduling ◽  
Cross Domain

scholarly journals Requirements for Cross-domain Knowledge Sharing in Collaborative Product-Service System Design

Procedia CIRP ◽  
2016 ◽  
Vol 47 ◽  
pp. 108-113 ◽  
Author(s):  
Stefan Wiesner ◽  
Fenareti Lampathaki ◽  
Evmorfia Biliri ◽  
Klaus-Dieter Thoben
Keyword(s):  
Knowledge Sharing ◽  
System Design ◽  
Domain Knowledge ◽  
Service System ◽  
Product Service System ◽  
Cross Domain ◽  
Product Service ◽  
Service System Design

scholarly journals Development of Knowledge Graph for Data Management Related to Flooding Disasters Using Open Data

2021 ◽  
Vol 13 (5) ◽  
pp. 124
Author(s):  
Jiseong Son ◽  
Chul-Su Lim ◽  
Hyoung-Seop Shim ◽  
Ji-Sun Kang
Keyword(s):  
Artificial Intelligence ◽  
Domain Knowledge ◽  
Open Data ◽  
Heterogeneous Data ◽  
Big Data Analysis ◽  
Knowledge Graph ◽  
Cross Domain ◽  
Disaster Data ◽  
Knowledge Graphs ◽  
Open Datasets

Despite the development of various technologies and systems using artificial intelligence (AI) to solve problems related to disasters, difficult challenges are still being encountered. Data are the foundation to solving diverse disaster problems using AI, big data analysis, and so on. Therefore, we must focus on these various data. Disaster data depend on the domain by disaster type and include heterogeneous data and lack interoperability. In particular, in the case of open data related to disasters, there are several issues, where the source and format of data are different because various data are collected by different organizations. Moreover, the vocabularies used for each domain are inconsistent. This study proposes a knowledge graph to resolve the heterogeneity among various disaster data and provide interoperability among domains. Among disaster domains, we describe the knowledge graph for flooding disasters using Korean open datasets and cross-domain knowledge graphs. Furthermore, the proposed knowledge graph is used to assist, solve, and manage disaster problems.

Rating Knowledge Sharing in Cross-Domain Collaborative Filtering

2015 ◽  
Vol 45 (5) ◽  
pp. 1068-1082 ◽  
Author(s):  
Bin Li ◽  
Xingquan Zhu ◽  
Ruijiang Li ◽  
Chengqi Zhang
Keyword(s):  
Knowledge Sharing ◽  
Collaborative Filtering ◽  
Cross Domain

scholarly journals Reducing the Search Space in Literature-Based Discovery by Exploring Outlier Documents: a Case Study in Finding Links Between Gut Microbiome and Alzheimer’s Disease

2017 ◽  
Vol 3 (3) ◽  
pp. 58 ◽  
Author(s):  
Bojan Cestnik ◽  
Elsa Fabbretti ◽  
Donatella Gubiani ◽  
Tanja Urbančič ◽  
Nada Lavrač
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Gut Microbiome ◽  
Domain Knowledge ◽  
Search Space ◽  
Easy Access ◽  
Literature Mining ◽  
Cross Domain ◽  
Scientific Papers ◽  
Literature Based Discovery

Literature-based discovery tools have been often used to overcome the problem of fragmentation of science and to assist researchers in their process of cross-domain knowledge discovery. In this paper we propose a methodology for cross-domain literature-based discovery that focuses on outlier documents to reduce the search space of potential cross-domain links and to improve search efficiency. In a previous study, literature mining tools OntoGen for document clustering and CrossBee for cross-domain bridging term exploration were combined to search for hidden relations in scientific papers from two different domains of interest, where the utility of the approach was demonstrated in a study involving PubMed papers about Alzheimer’s disease and gut microbiome. This paper extends the approach by proposing a methodology, implemented as a repeatable workflow in a web-based text mining platform TextFlows, which enables easy access and execution of the methodology for the interested researcher.

scholarly journals Assessing Large-Scale, Cross-Domain Knowledge Bases for Semantic Search

2020 ◽  
Vol 39 (3) ◽  
pp. 595-602
Author(s):  
Aatif Ahmad Khan ◽  
Sanjay Kumar Malik
Keyword(s):  
Domain Knowledge ◽  
Large Scale ◽  
Open Data ◽  
Knowledge Bases ◽  
Semantic Search ◽  
Semantic Web Technologies ◽  
Data Format ◽  
Web Technologies ◽  
Cross Domain ◽  
Description Framework

Semantic Search refers to set of approaches dealing with usage of Semantic Web technologies for information retrieval in order to make the process machine understandable and fetch precise results. Knowledge Bases (KB) act as the backbone for semantic search approaches to provide machine interpretable information for query processing and retrieval of results. These KB include Resource Description Framework (RDF) datasets and populated ontologies. In this paper, an assessment of the largest cross-domain KB is presented that are exploited in large scale semantic search and are freely available on Linked Open Data Cloud. Analysis of these datasets is a prerequisite for modeling effective semantic search approaches because of their suitability for particular applications. Only the large scale, cross-domain datasets are considered, which are having sizes more than 10 million RDF triples. Survey of sizes of the datasets in triples count has been depicted along with triples data format(s) supported by them, which is quite significant to develop effective semantic search models.

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